I've spent hours browsing everywhere now to try to create a multiindex from dataframe in pandas. This is the dataframe I have (posting excel sheet mockup. I do have this in pandas dataframe):
And this is what I want:
I have tried
newmulti = currentDataFrame.set_index(['user_id','account_num'])
But it returns a dataframe, not a multiindex. Also, I could not figure out how to make 'user_id' level 0 and 'account_num' level 1. I think this must be trivial but I've read so many posts, tutorials, etc. and still could not figure it out. Partly because I'm a very visual person and most posts are not. Please help!
You could simply use groupby in this case, which will create the multi-index automatically when it sums the sales along the requested columns.
df.groupby(['user_id', 'account_num', 'dates']).sales.sum().to_frame()
You should also be able to simply do this:
df.set_index(['user_id', 'account_num', 'dates'])
Although you probably want to avoid any duplicates (e.g. two or more rows with identical user_id, account_num and date values but different sales figures) by summing them, which is why I recommended using groupby.
If you need the multi-index, you can simply access viat new_df.index where new_df is the new dataframe created from either of the two operations above.
And user_id will be level 0 and account_num will be level 1.
For clarification of future users I would like to add the following:
As said by Alexander,
df.set_index(['user_id', 'account_num', 'dates'])
with a possible inplace=True does the job.
The type(df) gives
pandas.core.frame.DataFrame
whereas type(df.index) is indeed the expected
pandas.core.indexes.multi.MultiIndex
Use pd.MultiIndex.from_arrays
lvl0 = currentDataFrame.user_id.values
lvl1 = currentDataFrame.account_num.values
midx = pd.MultiIndex.from_arrays([lvl0, lvl1], names=['level 0', 'level 1'])
There are two ways to do it, albeit not exactly like you have shown, but it works.
Say you have the following df:
A B C D
0 nil one 1 NaN
1 bar one 5 5.0
2 foo two 3 8.0
3 bar three 2 1.0
4 foo two 4 2.0
5 bar two 6 NaN
1. Workaround 1:
df.set_index('A', append = True, drop = False).reorder_levels(order = [1,0]).sort_index()
This will return:
2. Workaround 2:
df.set_index(['A', 'B']).sort_index()
This will return:
The DataFrame returned by currentDataFrame.set_index(['user_id','account_num']) has it's index set to ['user_id','account_num']
newmulti.index will return the MultiIndex object.
Related
I've spent hours browsing everywhere now to try to create a multiindex from dataframe in pandas. This is the dataframe I have (posting excel sheet mockup. I do have this in pandas dataframe):
And this is what I want:
I have tried
newmulti = currentDataFrame.set_index(['user_id','account_num'])
But it returns a dataframe, not a multiindex. Also, I could not figure out how to make 'user_id' level 0 and 'account_num' level 1. I think this must be trivial but I've read so many posts, tutorials, etc. and still could not figure it out. Partly because I'm a very visual person and most posts are not. Please help!
You could simply use groupby in this case, which will create the multi-index automatically when it sums the sales along the requested columns.
df.groupby(['user_id', 'account_num', 'dates']).sales.sum().to_frame()
You should also be able to simply do this:
df.set_index(['user_id', 'account_num', 'dates'])
Although you probably want to avoid any duplicates (e.g. two or more rows with identical user_id, account_num and date values but different sales figures) by summing them, which is why I recommended using groupby.
If you need the multi-index, you can simply access viat new_df.index where new_df is the new dataframe created from either of the two operations above.
And user_id will be level 0 and account_num will be level 1.
For clarification of future users I would like to add the following:
As said by Alexander,
df.set_index(['user_id', 'account_num', 'dates'])
with a possible inplace=True does the job.
The type(df) gives
pandas.core.frame.DataFrame
whereas type(df.index) is indeed the expected
pandas.core.indexes.multi.MultiIndex
Use pd.MultiIndex.from_arrays
lvl0 = currentDataFrame.user_id.values
lvl1 = currentDataFrame.account_num.values
midx = pd.MultiIndex.from_arrays([lvl0, lvl1], names=['level 0', 'level 1'])
There are two ways to do it, albeit not exactly like you have shown, but it works.
Say you have the following df:
A B C D
0 nil one 1 NaN
1 bar one 5 5.0
2 foo two 3 8.0
3 bar three 2 1.0
4 foo two 4 2.0
5 bar two 6 NaN
1. Workaround 1:
df.set_index('A', append = True, drop = False).reorder_levels(order = [1,0]).sort_index()
This will return:
2. Workaround 2:
df.set_index(['A', 'B']).sort_index()
This will return:
The DataFrame returned by currentDataFrame.set_index(['user_id','account_num']) has it's index set to ['user_id','account_num']
newmulti.index will return the MultiIndex object.
I have a function which sends automated messages to clients, and takes as input all the columns from a dataframe like the one below.
name
phone
status
date
name_1
phone_1
sending
today
name_2
phone_2
sending
yesterday
I iterate through the dataframe with a pandas apply (axis=1) and use the values on the columns of each row as inputs to my function. At the end of it, after sending, it changes the status to "sent". The thing is I only want to send to the clients whose date reference is "today". Now, with pandas.apply(axis=1) this is perfectly doable, but in order to slice the clients with "today" value, I need to:
create a new dataframe with today's value,
remove it from the original, and then
reappend it to the original.
I thought about running through the whole dataframe and ignore the rows which have dates different than "today", but if my dataframe keeps growing, I'm afraid of the whole process becoming slower.
I saw examples of this being done with mask, although usually people only use 1 column, and I need more than just the one. Is there any way to do this with pandas apply?
Thank you.
I think you can use .loc to filter the data and apply func to it.
In [13]: df = pd.DataFrame(np.random.rand(5,5))
In [14]: df
Out[14]:
0 1 2 3 4
0 0.085870 0.013683 0.221890 0.533393 0.622122
1 0.191646 0.331533 0.259235 0.847078 0.649680
2 0.334781 0.521263 0.402030 0.973504 0.903314
3 0.189793 0.251130 0.983956 0.536816 0.703726
4 0.902107 0.226398 0.596697 0.489761 0.535270
if we want double the values of rows where the value in first column > 0.3
Out[16]:
0 1 2 3 4
2 0.334781 0.521263 0.402030 0.973504 0.903314
4 0.902107 0.226398 0.596697 0.489761 0.535270
In [18]: df.loc[df[0] > 0.3] = df.loc[df[0] > 0.3].apply(lambda x: x*2, axis=1)
In [19]: df
Out[19]:
0 1 2 3 4
0 0.085870 0.013683 0.221890 0.533393 0.622122
1 0.191646 0.331533 0.259235 0.847078 0.649680
2 0.669563 1.042527 0.804061 1.947008 1.806628
3 0.189793 0.251130 0.983956 0.536816 0.703726
4 1.804213 0.452797 1.193394 0.979522 1.070540
I am trying to implement a formula to create a new column in Dataframe using existing column but that column is a summation from 0 to a number present in some other column.
I was trying something like this:
dataset['B']=sum([1/i for i in range(dataset['A'])])
I know something like this would work
dataset['B']=sum([1/i for i in range(10)])
but I want to make this 10 dynamic based on some different column.
I keep on getting this error.
TypeError: 'Series' object cannot be interpreted as an integer
First of all, I should admit that I could not understand you question completely. However, what I understood that you want to iterate over the rows of a DataFrame and make a new column by doing some operation/s on that value.
Is that is so, then I would recommend you following link
Regarding TypeError: 'Series' object cannot be interpreted as an integer:
The init signature range() takes integers as input. i.e [i for i in range(10)] should give you [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]. However, if one of the value from your dataset['A'] is float, or not integer , this might result in the error you are having. Moreover, if you notice, the first value is a zero, as a result, 1/i should result in a different error. As a result, you might have to rewrite the code as [1/i for i in range (1 , row_value_of_dataset['A'])]
It will be highly appreciate if you could make an example of what you DataFrame might look like and what is your desired output. Then perhaps it is easier to post a solution.
BTW forgot to post what I understood from your question:
#assume the data:
>>>import pandas as pd
>>>data = pd.DataFrame({'A': (1, 2, 3, 4)})
#the data
>>>data
A
0 1
1 2
2 3
3 4
#doing operation on each of the rows
>>>data['B']=data.apply(lambda row: sum([1/i for i in range(1, row.A)] ), axis=1)
# Column B is the newly added data
>>>data
A B
0 1 0.000000
1 2 1.000000
2 3 1.500000
3 4 1.833333
Perhaps explicitly use cumsum, or even apply?
Anyway trying to move an array/list item directly into a dataframe and seem to view this as a dictionary. Try something like this, I've not tested it,
array_x = [x, 1/x for x in dataset.values.tolist()] # or `dataset.A.tolist()`
df = pd.DataFrame(data=(np.asarray(array_x)))
df.columns = [A, B]
Here the idea is to break the Series apart into a list, and input the list into a dataframe. This can be explicitly done without needing to go Series->list->dataframe and is not very efficient.
I am inputting multiple spreadsheets with multiple columns of data. For each spreadsheet, the maximum value of each column is found. Then, for each element in the column, the element is divided by the maximum value of that column. The output should be a value (between 0 and 1) for each element in the column in ascending order. This is appended to a list which should be added to the source spreadsheet as a column.
Currently, the nested loops are performing correctly apart from the final step, as far as I understand. Each column is added to the spreadsheet EXCEPT the values are for the final column of the source spreadsheet rather than values related to each individual column.
I have tried changing the indents to associate levels of the code with different parts (as I think this is the problem) and tried moving the appended column along in the dataframe, to no avail.
for i in distlist:
#listname = i[4:] + '_norm'
df2 = pd.read_excel(i,header=0,index_col=None, skip_blank_lines=True)
df3 = df2.dropna(axis=0, how='any')
cols = []
for column in df3:
cols.append(column)
for x in cols:
listname = x + ' norm'
maxval = df3[x].max()
print(maxval)
mylist = []
for j in df3[x]:
findNL = (j/maxval)
mylist.append(findNL)
df3[listname] = mylist
saveloc = 'E:/test/'
filename = i[:-18] + '_Normalised.xlsx'
df3.to_excel(saveloc+filename, index=False)
New columns are added to the output dataframe with bespoke headings relating to the field headers in the source spreadsheet and renamed according to (listname). The data in each one of these new columns is identical and relates to the final column in the spreadsheet. To me, it seems to be overwriting the values each time (as if looping through the entire spreadsheet, not outputting for each column), and adding it to the spreadsheet.
Any help would be much appreciated. I think it's something simple, but I haven't managed to work out what...
If I understand you correctly, you are overcomplicating things. You dont need a for loop for this. You can simplify your code:
# Make example dataframe, this is not provided
df = pd.DataFrame({'col1':[1, 2, 3, 4],
'col2':[5, 6, 7, 8]})
print(df)
col1 col2
0 1 5
1 2 6
2 3 7
3 4 8
Now we can use DataFrame.apply and use add_suffix to give the new columns _norm suffix and after that concat the columns to one final dataframe
df_conc = pd.concat([df, df.apply(lambda x: x/x.max()).add_suffix('_norm')],axis=1)
print(df_conc)
col1 col2 col1_norm col2_norm
0 1 5 0.25 0.625
1 2 6 0.50 0.750
2 3 7 0.75 0.875
3 4 8 1.00 1.000
Many thanks. I think I was just overcomplicating it. Incidentally, I think my code may do the same job, but because there is so little difference in the values, it wasn't notable.
Thanks for your help #Erfan
I have a Pandas dataframe named data_match. It contains columns '_worker_id', '_unit_id', and 'caption'. (Please see attached screenshot for some of the rows in this dataframe)
Let's say the index column is not in ascending order (I want the index to be 0, 1, 2, 3, 4...n) and I want it to be in ascending order. So I ran the following function attempting to reset the index column:
data_match=data_match.reset_index(drop=True)
I was able to get the function to return the correct output in my computer using Python 3.6. However, when my coworker ran that function in his computer using Python 3.6, the '_worker_id' column got removed.
Is this due to the (drop=True) clause next to reset_index? But I didn't know why it worked in my computer and not in my coworker's computer. Can anybody advise?
As the saying goes, "What happens in your interpreter stays in your
interpreter". It's impossible to explain the discrepancy without seeing the
full history of commands entered into both Python interactive sessions.
However, it is possible to venture a guess:
df.reset_index(drop=True)
drops the current index of the DataFrame and replaces it with an index of
increasing integers. It never drops columns.
So, in your interactive session, _worker_id was a column. In your co-worker's
interactive session, _worker_id must have been an index level.
The visual difference can be somewhat subtle. For example, below, df has a
_worker_id column while df2 has a _worker_id index level:
In [190]: df = pd.DataFrame({'foo':[1,2,3], '_worker_id':list('ABC')}); df
Out[190]:
_worker_id foo
0 A 1
1 B 2
2 C 3
In [191]: df2 = df.set_index('_worker_id', append=True); df2
Out[191]:
foo
_worker_id
0 A 1
1 B 2
2 C 3
Notice that the name _worker_id appears one line below foo when it is an
index level, and on the same line as foo when it is a column. That is the only
visual clue you get when looking at the str or repr of a DataFrame.
So to repeat: When _worker_index is a column, the column is unaffected by
df.reset_index(drop=True):
In [194]: df.reset_index(drop=True)
Out[194]:
_worker_id foo
0 A 1
1 B 2
2 C 3
But _worker_index is dropped when it is part of the index:
In [195]: df2.reset_index(drop=True)
Out[195]:
foo
0 1
1 2
2 3